Silent-Ischemia 发表于 2025-3-28 18:20:43
http://reply.papertrans.cn/103/10216/1021545/1021545_41.png流眼泪 发表于 2025-3-28 20:31:24
http://reply.papertrans.cn/103/10216/1021545/1021545_42.pngExaggerate 发表于 2025-3-28 23:44:11
X-ray Prohibited Items Recognition Based on Improved YOLOv5 problem of overlapping occlusion of multi-scale contraband. Experimental results in the real X-ray prohibited items dataset demonstrate that our model outperforms state-of-the-art methods in terms of detection accuracy.半球 发表于 2025-3-29 04:59:21
http://reply.papertrans.cn/103/10216/1021545/1021545_44.png菊花 发表于 2025-3-29 11:07:48
Temporal Convolution and Multi-Attention Jointly Enhanced Electricity Load Forecastingssign different weight values to each timestep. We validate the effectiveness of our method using three real datasets. The results show that our model performs excellent results compared to traditional deep learning models.Narcissist 发表于 2025-3-29 13:52:11
Temporal Convolution and Multi-Attention Jointly Enhanced Electricity Load Forecastingssign different weight values to each timestep. We validate the effectiveness of our method using three real datasets. The results show that our model performs excellent results compared to traditional deep learning models.CHANT 发表于 2025-3-29 17:55:25
Rule-Enhanced Evolutional Dual Graph Convolutional Network for Temporal Knowledge Graph Link Predictlutional network is employed to capture the structural dependency of relations and the temporal dependency across adjacent snapshots. We conduct experiments on four real-world datasets. The results demonstrate that our model outperforms the baselines, and enhancing information in snapshots is benefiiodides 发表于 2025-3-29 21:08:22
Rule-Enhanced Evolutional Dual Graph Convolutional Network for Temporal Knowledge Graph Link Predictlutional network is employed to capture the structural dependency of relations and the temporal dependency across adjacent snapshots. We conduct experiments on four real-world datasets. The results demonstrate that our model outperforms the baselines, and enhancing information in snapshots is benefi绝食 发表于 2025-3-30 03:27:47
DINE: Dynamic Information Network Embedding for Social Recommendation users and items simultaneously and integrate the representations in dynamic and static information networks. In addition, the multi-head self-attention mechanism is employed to model the evolution patterns of dynamic information networks from multiple perspectives. We conduct extensive experimentsToxoid-Vaccines 发表于 2025-3-30 05:26:19
http://reply.papertrans.cn/103/10216/1021545/1021545_50.png